K8 Site Reliability SME

You run the control plane where AIOps meets tenants — where topology-aware scheduling, self-healing, and agent-driven remediation actually execute.

NeoCloud is building an AI-operated GPU cloud. Kubernetes is where all of that lands on real customer workloads: the topology-aware scheduler places jobs on the right NVLink domain, the operator drains and reschedules around predicted faults, and the tenant boundary is enforced against a Bare-Metal-as-a-Service backend. In this role you design, deploy, and operate that control plane — and you make sure the AIOps substrate can reach in and remediate without a human on the pager.

• Production Kubernetes clusters optimized for GPU workloads at scale (100–10,000 GPUs).
• Nvidia GPU operator, device plugin, MIG configuration, and GPU time-slicing policies.
• Topology-aware scheduling: GPU locality, NVLink domain awareness, network rail affinity.
• Custom Resource Definitions (CRDs) for GPU workload lifecycle management.
• AI framework integrations: Slurm on K8S, Ray on K8S, Kubeflow.
• Multi-tenant isolation: namespaces, network policies, resource quotas, RBAC, pod security standards.
• Bare-Metal as a Service (BMaaS): automated provisioning, tenant onboarding, lifecycle, reclamation.
• Terraform providers and modules for infrastructure-as-code across GPU clusters.
• SLIs/SLOs for cluster availability, job completion rates, and provisioning latency.
• Incident management: runbook automation, escalation, post-incident reviews.
• Monitoring stack: Prometheus, Grafana, Alertmanager, PagerDuty.
• GPU node failure handling: automated detection, drain/cordon/taint, workload rescheduling.

• The remediation-actuator and workflow engine land here — you make the control plane safe for automated action.
• Your CRDs are the schema the platform's predictors and remediators write against.
• Every human intervention you do this quarter becomes an autonomous workflow next quarter.

• Automated drain/reschedule around predicted GPU faults, at scale, without customer impact.
• BMaaS live for external tenants with self-service onboarding.
• Cluster availability and job-completion SLOs published and met.

• 5+ years in Kubernetes operations, with at least 2 years managing GPU workloads on K8S
• Deep understanding of Nvidia GPU operator, device plugin, and GPU scheduling in K8S
• Experience with topology-aware scheduling and GPU-specific resource management
• Hands-on experience building multi-tenant K8S platforms with strong isolation guarantees
• Experience with bare-metal server provisioning and lifecycle automation (Ironic, MAAS, or custom)
• Proficiency in Terraform, Helm, and GitOps workflows (ArgoCD/Flux)
• Strong SRE background: SLI/SLO frameworks, incident management, capacity planning
• Experience with Prometheus, Grafana, and alerting at scale
• Strong programming skills in Go or Python for operator/CRD development
• AIOps aptitude — you think of the K8S control plane as an execution surface for automated remediation, not just a scheduler. You've either wired an autoscaler/remediator loop into K8S or you can design one.
• Runbook-as-code mindset — every SRE playbook you write should be executable by the platform.

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